Discussions on sparse feature sampling.
Our model transformation shows that sparse feature sampling from local areas is critical to data efficiency.
Though PnP-DETR [1]} also attempts to sample sparse features, the features are sampled from the entire image, instead of a local area in the image. As a result, the sampled features may contain multiple instances and the model still has to learn to focus on specific objects from more training data. By contrast, all Sparse RCNN, DeformDETR, and our method sample sparse features from local object regions, thus alleviating the data-hungry issue.
